Discovering Hierarchical Processes Using Flexible Activity Trees for Event Abstraction

• URL: http://arxiv.org/abs/2010.08302v1
• Date: Fri, 16 Oct 2020 10:50:41 GMT
• Title: Discovering Hierarchical Processes Using Flexible Activity Trees for Event Abstraction
• Authors: Xixi Lu, Avigdor Gal, Hajo A. Reijers
• Abstract summary: We propose FlexHMiner, a three-step approach to discover processes with multi-level interleaved subprocesses. We used seven real-life logs to compare the qualities of hierarchical models discovered using domain knowledge, random clustering, and flat approaches. Our results indicate that the hierarchical process models that the FlexHMiner generates compare favorably to approaches that do not exploit hierarchy.
• Score: 7.754062965937491
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Processes, such as patient pathways, can be very complex, comprising of hundreds of activities and dozens of interleaved subprocesses. While existing process discovery algorithms have proven to construct models of high quality on clean logs of structured processes, it still remains a challenge when the algorithms are being applied to logs of complex processes. The creation of a multi-level, hierarchical representation of a process can help to manage this complexity. However, current approaches that pursue this idea suffer from a variety of weaknesses. In particular, they do not deal well with interleaving subprocesses. In this paper, we propose FlexHMiner, a three-step approach to discover processes with multi-level interleaved subprocesses. We implemented FlexHMiner in the open source Process Mining toolkit ProM. We used seven real-life logs to compare the qualities of hierarchical models discovered using domain knowledge, random clustering, and flat approaches. Our results indicate that the hierarchical process models that the FlexHMiner generates compare favorably to approaches that do not exploit hierarchy.

Related papers

• Grouping Local Process Models [0.19036571490366497]
  Local Process Model (LPM) discovery tries to build a set of LPMs that explain sub-behaviors of the process. This work proposes a three-step pipeline for grouping similar LPMs using various process model similarity measures.
  arXiv  Detail & Related papers  (2023-11-06T11:24:27Z)
• Discovering Hierarchical Process Models: an Approach Based on Events Clustering [0.0]
  We present an algorithm for discovering hierarchical process models represented as two-level workflow nets. Unlike existing solutions, our algorithm does not impose restrictions on the process control flow and allows for iteration.
  arXiv  Detail & Related papers  (2023-03-12T11:05:40Z)
• Trace Encoding in Process Mining: a survey and benchmarking [0.34410212782758054]
  Methods are employed across several process mining tasks, including predictive process monitoring, anomalous case detection, clustering trace, etc. Most papers choose existing encoding methods arbitrarily or employ a strategy based on a specific expert knowledge domain. This work aims at providing a comprehensive survey on event log encoding by comparing 27 methods.
  arXiv  Detail & Related papers  (2023-01-05T17:25:30Z)
• Clustering Object-Centric Event Logs [0.36748639131154304]
  We propose a clustering-based approach to cluster similar objects in OCELs to simplify the obtained process models. Our approach reduces the complexity of the process models and generates coherent subsets of objects which help the end-users gain insights into the process.
  arXiv  Detail & Related papers  (2022-07-26T09:16:39Z)
• Autoregressive Search Engines: Generating Substrings as Document Identifiers [53.0729058170278]
  Autoregressive language models are emerging as the de-facto standard for generating answers. Previous work has explored ways to partition the search space into hierarchical structures. In this work we propose an alternative that doesn't force any structure in the search space: using all ngrams in a passage as its possible identifiers.
  arXiv  Detail & Related papers  (2022-04-22T10:45:01Z)
• Human Activity Recognition using Attribute-Based Neural Networks and Context Information [61.67246055629366]
  We consider human activity recognition (HAR) from wearable sensor data in manual-work processes. We show how context information can be integrated systematically into a deep neural network-based HAR system. We empirically show that our proposed architecture increases HAR performance, compared to state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-10-28T06:08:25Z)
• Procedures as Programs: Hierarchical Control of Situated Agents through Natural Language [81.73820295186727]
  We propose a formalism of procedures as programs, a powerful yet intuitive method of representing hierarchical procedural knowledge for agent command and control. We instantiate this framework on the IQA and ALFRED datasets for NL instruction following.
  arXiv  Detail & Related papers  (2021-09-16T20:36:21Z)
• CoCoMoT: Conformance Checking of Multi-Perspective Processes via SMT (Extended Version) [62.96267257163426]
  We introduce the CoCoMoT (Computing Conformance Modulo Theories) framework. First, we show how SAT-based encodings studied in the pure control-flow setting can be lifted to our data-aware case. Second, we introduce a novel preprocessing technique based on a notion of property-preserving clustering.
  arXiv  Detail & Related papers  (2021-03-18T20:22:50Z)
• Process Comparison Using Object-Centric Process Cubes [69.68068088508505]
  In real-life business processes, different behaviors exist that make the overall process too complex to interpret. Process comparison is a branch of process mining that isolates different behaviors of the process from each other by using process cubes. We propose a process cube framework, which supports process cube operations such as slice and dice on object-centric event logs.
  arXiv  Detail & Related papers  (2021-03-12T10:08:28Z)
• Process Discovery for Structured Program Synthesis [70.29027202357385]
  A core task in process mining is process discovery which aims to learn an accurate process model from event log data. In this paper, we propose to use (block-) structured programs directly as target process models. We develop a novel bottom-up agglomerative approach to the discovery of such structured program process models.
  arXiv  Detail & Related papers  (2020-08-13T10:33:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.